Tuesday, March 28, 2017

How the Internet of Things is changing the face of retail

By: Ali Newton

This article was originally published on SmallBusiness.co.uk

There hasn’t been an advancement in retail as drastic as the IoT revolution since the Industrial Revolution. The Internet of Things (IoT) is the idea that everyday objects can be connected in the same way that computers are today. And, with consumer adoption of IoT devices on the rise, now is the perfect time for retailers to get informed and capitalize on the IoT.

Whether it is to improve their overall customer experience, or to create new revenue streams, the IoT truly is changing the face of retail. Here are three ways the IoT could be integrated into every aspect of retail — from store displays, to storage equipment, to the shop floor.

1. Smart shelves

Panasonic is currently developing a product called the Powershelf. These shelves have built-in sensor technology that keeps track of inventory in real-time, saving businesses thousands of pounds in paid hours that they could reinvest elsewhere.

However, Powershelves also have the potential to be extremely useful on shop floors, as they can collect data about shoppers based on the products that they have chosen. In addition, these shelves give customers real-time prices that are based on demand. The shelf labels are wireless and can update prices based on the quantities that are left. The shelves can also detect when the products are about to go out of date, and alter the price according to this information too.
Jobs like stock counting, market research and stock replenishment can take human workers hours. Alternatively, they could be automatically performed by Powershelves talking to each other via the IoT.

2. In-store beacon tech

In-store beacons were set to become very popular for a while, but they haven’t quite caught on as previously anticipated. Beacons rely on customers coming within proximity of a shop, at which point they can be sent a message or an email to encourage them to come into the store — provided that the shop already has their contact details.

Still, it’s a solid idea in principle. A ’10 per cent offer when you buy today’ push notification could be sent to the consumers’ mobiles as an incentive to lure them into a shop if they’re nearby.
The issue with beacon technology is that it relies on Bluetooth, which many consumers don’t have switched on as it is known to drain battery power. In addition, customers usually need to have the brand’s app downloaded too. This places several obstacles in the way of the retailer before it can contact the customer directly.

Despite these obstacles, many brands are using proximity marketing to help drive their retail sales.

3. Smart shopping carts and cashless stores

IoT is a powerful tool for brick and mortar shops to compete with eCommerce stores that are taking over the retail world. Walmart recently began to develop shopping carts that can drive themselves to help customers find their way around its shops. It is also working on a technology that allows customers to order online and get their shopping delivered by a driverless cart directly to their car, or Uber, in the car park.

Similarly, Amazon’s Seattle shop has no checkouts. Customers simply enter the shop, pick up the items they need off the store display, and leave. Sensors around the shop record the items that customers pick up, removing the need for them to check out.

Whether or not any of these ideas will become an integral part of retail’s future remains to be seen. Predicting the future is always difficult and businesses and individuals are right to be skeptical of anyone telling them that the future is going to be radically different because of the IoT.

However, just because people should be skeptical about the idea that the IoT may change retail entirely, it doesn’t mean that they should write the idea off altogether. One IoT development is unlikely to change retail on its own, but as more of these technologies enter the market and they become more affordable, a greater impact will begin to be seen throughout retail.

Monday, March 27, 2017

Is Amazon in the Room?

By: Laura Sigman

This post was originally published on the LightSpeed Research blog.

On a recent earnings call, Sir Martin Sorrell, CEO of Lightspeed’s parent company WPP, talked about what keeps him up at night. And no; it’s not (necessarily) his infant daughter – it’s Amazon.

“And I would just mention the rise of Amazon, because in answer to the question, my favorite question is what worries you when you go to bed at night and when you wake up in the morning. It's not a three-month-old child (laughter), it's Amazon, which is a child still, but not three months. And Amazon's penetration of most areas is frightening, if not terrifying to some, and I think there is a battle brewing between Google and Amazon.”

The fear mostly seems to be of the unknown, as Amazon is thought to be quietly pursuing an advertising strategy carefully away from the watchful eyes of Wall Street.

Is Amazon really committed? They are by pure virtue of their strategically evolving business model. By being among the first big players on the e-commerce scene, they cemented their early adapter consumers to them. They’ve grown a multimedia offer around their core competency, and now Amazon knows not only what we read, but what we search for, what we buy, what we watch, what we listen to. I’m an Amazon Prime customer, and I take advantage of all of the bells and whistles that come along with it. So they know what content I’m engaging with, and whether I’m connecting to the content from my PC, smartphone, tablet or Alexa. And they can leverage this vast supply of shopper and behavioral data to sell hyper-targeted advertising to brands who can then speak directly to me.

When you look at it like that, it’s really not much different than how we’ve worked in the panel world. Historically, we have facilitated the conversations brands have with consumers, and have evolved by taking advantage of emerging technologies to help amplify those conversations. And, like Amazon, we grew our business by embracing early on that panelists (consumers) are people, too. 

(Believe it or not, it’s not as obvious to everyone as that sounds!) Today’s consumers want to have meaningful interactions, but they also want to have them when and where is convenient to them. So we meet them on their devices of choice; we always design surveys mobile-first (in fact, Lightspeed has an entire team dedicated to this) and we use data appends to reach the right consumer with the right questions. We invite survey respondents to answer open-ends with video responses – an engaging experience for them resulting in more meaningful data for brands to act on. We’re able to blur the line between quant and qual, intercepting surveys with invites to participate in deeper, on-point conversations. And brands can leverage all of this to create hyper-targeted advertising that speaks directly to their consumers. Which ties back to that Amazon example I shared above.

As Kantar pointed out at their FragmentNation event, the marketplace is splintering -- not with a whimper but with a bang. So while the ad world should fear the Amazon in the room, it should also embrace it. It’s an eye-opening reminder that consumers are advertising’s most valuable assets in a marketplace that is more diverse and fragmented than ever.

Wednesday, March 22, 2017

Here Comes Gen Z: 10 Keys to Understanding Them

According to Open Mind Strategy research, these are the top things to know about the new kids on the block Gen Z:

1. Huge
Gen Zs make up more than a third of the world’s population and comprise nearly a quarter of the US population – bigger than both Millennials and Baby Boomers – and still being born.

2. The most diverse generation ever
Gen Z will be the last majority-White generation born in the United States. Already the white majority is holding on by a thread, only 51% of Gen Z born into non-Hispanic White families.
This generation’s diversity also extends to their sexuality and gender identity. More than one-third of Gen Zs self-identify as bisexual to some degree; more than half know someone who uses gender-neutral pronouns.

3. They idolize Influencers, not Celebrities
Most dedicate more time to YouTube than any other social site and their view of celebrities isn’t limited to movie stars and musicians, note the billions of views racked up by YouTube stars RayWilliamJohnson and PewDiePie. They want to emulate self-made Influencers who are just like them.

4. A plan to get paid
While Gen Zs are certainly passion-driven, if they know their passions won’t lead to financial stability, they have a plan for something that will. In everything from entrepreneurship to sports, kids and teens are finding places to excel early and focus their efforts in hopes of a payoff.

5. Having safe fun
Gen Zs are still teenagers! They want to have a good time, but they don’t want to negatively impact the successful future they are working to build. The teen pregnancy and birth rate are at historic lows, as is the usage of cigarettes and heroin among high-schoolers.

6. Caring about “cool”
Gen Z is snarky and very image aware. With the ever-growing influence of social media, there is a palpable return of “cool kids” and “losers” among Gen Z. They will quickly take down a post that doesn’t receive enough likes for fear of someone seeing its lack of attention.

7. Don’t share everything online
Gen Z takes a crafted and curated approach to posts. They are more aware of who they are sharing their lives with and how it affects their identity, which is why platforms like Snapchat are so appealing. They saw the devastating effects party pics had on their sibling’s scholarship or job offer.

8. No Mo “Beta Boys”
Gen Z boys want to be taken more seriously. To them, girls are certainly equal, but not better. Gen Z boys want in on the partnership by taking themselves a bit more seriously in school, work and relationships, but also embracing their sensitive side.

9. Mostly cynical
Gen Zs have realistic expectations and are skeptical that the world will work in their favor. More than eight in 10 Gen Zs were born after September 11. Growing up, conflicts over issues like the economy, gun violence and climate change, have been common. As a result, these teens have developed a valid claim to cynicism.

10. Still KIDS!
This generation is just beginning to come of age, and as uptight as they may seem, they’re still kids who haven’t quite figured it all out yet. They’re working hard and taking themselves seriously, but they are still silly, young, fun and undeclared.

Open Mind Strategy, LLC, is a research and brand strategy firm founded by Robin Hafitz, in 2010, with the mission of providing “more human intelligence.” OMS (http://www.openmindstrategy.com/) provides insight services, including qualitative and quantitative research, brand studies, show and message testing, segmentation, and customized inquiries, as well as strategic brand consulting and educational workshops. The OMS team is proud to have worked with leading clients, such as A&E Networks, AMC, Amazon, Clear Channel, Condé Nast, Gannett, Kao Brands, MTV, NBCUniversal, Scripps Networks, Unilever, USA Today, Yahoo!, and many more.

Monday, March 20, 2017

Online Ad Effectiveness Research Grows Up

 This article is brought to you by Survata.

The days of giving digital a pass are over. It’s time to grow up.”- Marc Pritchard, Chief Branding Officer, Procter & Gamble, January 2017

When the CBO of P&G tells us to grow up, we listen. And after speaking with clients at last month’s Media Insights Conference, it’s clear that there’s consensus: online advertising research needs to get more sophisticated.

We’re here to help. IAB breaks research down into phases: design, recruitment & deployment, and optimization. We’ll walk through each phase and determine what’s most in need of “growing up.” We’ll also include questions to ask your research partner to help increase the sophistication of your ad effectiveness research.


Let’s start by acknowledging that statistically sound online ad effectiveness research has not been easy to implement at reasonable cost until recently. As IAB notes, “Questions around recruitment, sample bias and deployment are hampering the validity of this research and undermining the industry as a whole.”

Just because perfect research design is challenging to achieve doesn’t mean that advertisers should settle for studies with debilitating flaws, leading to biased, unreliable results. In addition to challenges inherent to good research design, most ad effectiveness research partners have systematic biases due to the way they find respondents, which must be accounted for in the design phase. There has been innovation in this space within the past year using technology to reduce or eliminate systematic bias in respondent recruitment. 

Assuming you’re able to address the systematic bias of your research partner’s sampling, the major remaining challenge is how you approach the control group. At Survata, we think about this as a hierarchy: 
Using a holdout group is best practice, but implementing it requires spending some portion of your ad budget strictly on the control group. In other words, some of your ad budget will be spent on intentionally NOT showing people an ad. A small portion of people in the ad buy will instead be shown public service announcements to establish the control group. We love the purity of this approach, but we also understand the reality of advertising budgets. We don’t view holdout as a requirement for sound online ad effectiveness research. Smart design combined with technology can achieve methodologically sound control groups without “wasting” ad budget.

Along those lines, the Audience Segment approach has become de facto best practice for many of our clients. Basically, you create your control group from the same audience segment that you’re targeting in the ad buy. This isn’t perfect, as there could be an underlying reason that some people in the segment saw the ad but others didn’t (e.g., some people very rarely go online, or to very few websites), but it’s still an excellent approach. It’s the grown-up version of Demographic Matching.

Demographic Matching, in which the control group is created by matching as many demographic variables as possible with the exposed group (e.g., gender, age, income), is still a very common strategy. It’s straightforward to accomplish even using old online research methodologies. As online data has allowed us to learn far more useful information about consumers than demographic traits, this approach is dated.

Simply sampling GenPop as a control is undesirable. The results are much more likely to reveal the differences between the exposed and control groups than the effectiveness of the advertising.

Questions for your research partner:
  • What are known biases among respondents due to recruitment strategy?
  • What is your total reach? What percentage of the target group is within your reach? Is it necessary to weight low-IR population respondents due to lack of scale?
  • What’s your approach to creating control groups for online ad effectiveness research?
  • For Demographic Matching, how do you determine which demographic characteristics are most important to match?
  • How do you accomplish Audience Segment matching?
Recruitment/ Deployment

Historically, there were four methods to recruit respondents / deploy the survey: panels, intercepts, in-banner, or email list. To stomach these methodologies, researchers had to ignore one of the following flaws: non-response bias, misrepresentation, interruption of the customer experience or email list atrophy. In our view, these methodologies are now dated since the advent of the publisher network methodology.

The publisher network works by offering consumers content, ad-free browsing, or other benefits (e.g. free Wi-Fi) in exchange for taking a survey. The survey is completed as an alternative to paying for the content or service after the consumer organically visits the publisher. In addition to avoiding the flaws of the old methodologies, the publisher network model provides dramatically increased accuracy, scale, and speed.

Questions for your research partner:
  • What incentives are offered in exchange for respondent participation?
  • What are the attitudinal, behavioral, and demographic differences between someone willing to be in a panel versus someone not interested in being in a panel?
  • What are the attitudinal, behavioral, and demographic differences between someone willing to take a site intercept survey versus someone not interested in taking a site intercept survey?
  • How much does non-response bias affect the data?
  • Are you integrated with the client’s DMP?
  • How long to get the survey into the field, and how long until completed?
  • How does the vendor ensure that exposure bias doesn’t occur?
  • How does the vendor account for straight-liners, speeders, and other typical data quality issues?

An optimal ad effectiveness campaign returns results quickly, so that immediate and continuous adjustments can be made to replace poorly performing creative, targeting, and placements with higher performing ones. We call this real-time spend allocation. It’s analogous to real-time click-through rate optimization, as it relies on solutions to the same math problem (known as 
the multi-armed bandit).

By integrating with DMPs, ad effectiveness research can be cross-tabbed against even more datasets. The results will yield additional insights about a company’s existing customers.

Questions for your research partner:
  • Are results reported real-time?
  • How much advertising budget is wasted due to non-optimization?
  • How can DMP data be incorporated to improve ad research?

Flawed research methodologies can’t grow up, they can only continue to lower prices for increasingly suspect data. For online ad effectiveness research to grow up, new methodologies must be adopted.

To learn more about conducting your own ad effectiveness study, visit Survata

Monday, March 13, 2017

Must See Talks from KNect365’s Spring Insights 2017 Events

From former gang leaders, to cyborg anthropologists, to biomimicry experts- KNect365’s Must See Talks will challenge you to look at problems in a whole new way and become an ignitor of change for your organization.

“The Centrality of a Detailed Understanding of your Audience” – Haile Owusu, Chief Data Scientist, Mashable
Marketing Analytics & Data Science
April 3-5, 2017
San Francisco, CA
Use code MADS17LI for $100 off.
Buy tickets to see Haile: https://goo.gl/YqXZdx

“The Consumer Influence – and Impact – of Virtual Reality” – Jeremy Bailenson, Founding Director of Stanford University’s Virtual Human Interaction Lab at Stanford University
TMRE in Focus
May 1-3, 2017
Chicago, IL
Use code FOCUS17LI for $100 off.
Buy tickets to see Jeremy: https://goo.gl/c2UdIv

“Originals: How Non-Conformists Rule the World” – Adam Grant, Professor, Author of Give and Take and Originals at The Wharton School of Business at the University of Pennsylvania
June 20-22, 2017
Minneapolis, MN
Use code OMNI17LI for $100 off.
Buy tickets to see Adam: https://goo.gl/oUB85g

“Underdogs, Misfits & the Art of Battling Giants” – Malcom Gladwell, Best-Selling Author of Outliers, The Tipping Point and David & Goliath
TMRE: The Market Research Event
October 22-25, 2017
Orlando, FL
Use code TMRE17LI for $100 off.
Buy tickets to see Malcom: https://goo.gl/gM7Dtv

We hope to see you this spring!


The KNect 365 Event Team

Wednesday, March 8, 2017

The Ruthless Efficiency of Algorithms is Advancing Digital Frontiers

We recently caught up with Alistair Croll, Visiting Executive at Harvard Business School as well as our Marketing Analytics & Data Science Conference keynote speaker, to discuss the state of marketing analytics and data science, and where it’s going in the future.

Today, Croll helps to accelerate startups, and works with some of the world’s biggest companies on business model innovation. As an entrepreneur, he co-founded Coradiant; the Year One Labs accelerator; and a many other startups. Not to mention, he’s a sought-after speaker, and has launched and chaired some of the world’s leading conferences on emerging technology, including Startupfest, Strata, Cloud Connect, and Pandemon.io. Croll is also the author of four books on technology and entrepreneurship, including the best-selling Lean Analytics, which has been translated into eight languages.

What is the state of the data science and analytics industry in 2017?

Croll: There is a realization that data itself doesn't lead to answers. This is really maturity: It's asking the right question that's hard. Big data is replacing business intelligence, but most of it is still being used to run reports and batch processes—rather than to find advantage or insight.

At the same time, feeding the corpus of data into learning algorithms holds promise. Those with the authority to do so are pointing machine learning at their data seta to find correlations, then testing those for causal relationships they can exploit.

What have been the biggest changes data science and analytics since you started your career?

Croll: I'm not an analyst by trade. But the biggest change is clear: once, we first defined the schema, then collected data. Now, we collect the data, then define the schema.

In other words, "Collect first, ask questions later." This is a huge difference, but it has sort of snuck up on us. It means we can iterate more, answering questions and adjusting our lines of inquiry.

Have the influx of social media and mobile made your job easier or harder?

Croll: More data sets mean more potential insights, but also more spurious correlations. So it's a two-edged sword.

How is data science and analytics transforming every industry right now?

Croll: The simple, and somewhat terrifying, truth is that AI gets unreasonably powerful, very quickly. Whether driving a car, or playing a video game, or diagnosing a disease, or optimizing the design of an aircraft part, algorithms are better than humans. They don't get tired; they make fewer mistakes; they don't take breaks.

And what do we feed such algorithms? Data. There is no industry that will not be changed by the ruthless efficiency of algorithms advancing its digital frontiers.

Why is data science considered the “sexiest job of the 21st century?”

Croll: Data science is the intersection of statistics, critical thinking, and engineering. It requires a sense of narrative, and the ability to build something. It's that element of engineering that distinguishes it from simple analytics, because it builds things that become products, or processes. Rather than running a report, it improves the report's results.

If big data is oil, data science is the refinery that makes it usable.

What is the biggest challenge in data science and analytics today?

Croll: We are still, sadly, trying to replace opinions with facts. My good friend Randy Smerik argues that there's no such thing as big data: An airline that knows you're running late fails to update your hotel; false positives about in credit card management.

His point is that while we have tremendous amounts of data, we seldom apply them to significantly improve the business or the customer experience because doing so means making fundamental changes to the organization, job descriptions, customer policies, and so on.

Where do you see data science and analytics moving in the next 5 years?

Croll: Democratization, with the help of smart agents. Pundits have been saying that for a long time, but in the last couple of years tools like Cortana, Google Now, Siri, and Alexa—as well as various chat interfaces like Slack, Sophos, and Skype—are going mainstream.

I also think that insurers will put significant pressure on companies to implement better analytics and algorithms because it will be too risky to do otherwise. If the organization can know everything about itself all the time, it will be expected to do so. "We didn't know this was happening" will no longer be an excuse. And consequently, algorithms that can parse all of that data and reduce risk will be mandatory.

Hear more from Alistair during his keynote session, “Don’t’ Get Duped by Data” at the Marketing Analytics & Data Science Conference April 3-5, 2017 in San Francisco, CA.

Data science and marketing analytics are transforming every industry. There is a reason why it is being called the sexiest job of the 21st century. Calling all professionals that want to harness analytics and data science! Do you realize how critical you are to the future of your organization? Learn more here: https://goo.gl/CbYosj

Use our exclusive Blog discount code MADS17BL for $100 off the current rate. Buy your tickets here: https://goo.gl/CbYosj

Monday, March 6, 2017

Using Geofencing to Observe Shopper Behavior

This post was originally published on the Research Now blog.

It is widely discussed that mobile opens up incredible opportunities for researchers. It is perhaps equally widely discussed that mobile provides challenges for researchers – especially those most reticent to part with, let’s say, more traditional approaches. I could think of a number of examples of this two-sided coin, but I’ll leave all of those, save one, for future discussions.

One that the industry needs to tackle head on is the use of geolocation for understanding shopper behavior. So much opportunity! But logistics and analysis is so hard (for many rooted in market research)! The notion of using geolocation itself for research is no longer new. Geofencing has been used to target people for research for several years – with the most commonly used methodologies centered around delivering a survey to someone when they are in a specific location or after they have left. In many cases this is a viable approach to understanding shoppers – and getting feedback close to the point of experience.

Personally, I’m a fan of targeted and efficient research engagements that ask people to recall their shopping behaviors before they forget them. But I am also a fan of not having to ask what we don’t really need to ask, for example who they are, where they shopped, and when. With this idea in mind, and wanting to piggyback on prior years of researching Americans’ Black Friday shopping habits, we looked to explore how geofencing could be effectively utilized to understand shoppers with minimal active engagement from them. So, last Fall, we brainstormed with Placecast and their savvy team of location-focused researchers on how we could shed new light onto shopping behaviors around this critical time period for retailers.

While we did end up asking some questions directly of people, we managed to glean a lot by matching our panelists’ location data with existing profiling attributes. We discovered, for example, that the most affluent Walmart shoppers came to the store on Black Friday when compared to days leading up to and following that day.

The most affluent shoppers also proved to shop early in the morning in the days immediately prior to and following Black Friday. Understanding who shops where and when is crucial to retailers and advertisers as they try to craft relevant messaging and promotions for holiday sales. Combining geolocation data and associated advanced analytics with known profiling attributes creates a compelling story about shopper behavior, one that can be layered with surveys and other data sources to provide actionable insights.

The industry has an opportunity here – to use geolocation data in a smart way and one that alleviates much of the survey burden often placed on participants.

Wednesday, March 1, 2017

The OmniShopper 2017 Full Keynote Lineup

You’ve already heard about some of the biggest changes we’ve made to OmniShopper for 2017 – moving the event to June, away from your summer vacations and changing the location to Minneapolis, home of the Mall of America, the retail mecca.

But, what you may not have heard about yet is the FULL keynote lineup – it’s completely different from what you’ve seen before. Covering everything from marketing in the Trump era, the future of retail, the human side of selling, data informed design and more:

·         Originals: How Non-Conformists Rule the World
Adam Grant, Professor, The Wharton School of Business at the University of Pennsylvania, Author, Give and Take and Originals
·         Marketing in the Trump Age: New Rules for a New Reality
Peter Horst, Former Chief Marketing Officer, The Hershey Company
·         Digital Humanism & Recoding Culture: Moving Toward the End of Demographics, Evolution of
·         Psychographics and the Rise of the Individual
Edwin Wong, VP Research & Insights, Buzzfeed
·         CX Sells: How to Win with the Human Side of Selling at Brick & Mortar
Bridget Brennan, CEO, Female Factor, Author, Why She Buys
·         Moments Matter... Make Yours Iconic
Soon Yu, Former Global Vice President of Innovation, VF Corp, Author, Iconic Advantage
·         Data Informed Design: How the Evolution of Data Science Has Permeated into Product Vision & Design
Charlie Burgoyne, Principal Director of Data Science, Frog Design
·         Winning in Her Purse: How the Rise of Technology has Caused Far-Reaching Disruption Even in the Most Ubiquitous Fashion and Life Accessory
Kelley Styring, Principal, InsightFarm
·         Panel: Shaping the Future of Retail with Science, Technology & Consumers
Lakshmi Venkataramani, Senior Director, Customer Insights & Analytics, Walmart eCommerce
J Lynn Martinez, Vice President & Team Lead Kroger, Dr Pepper Snapple Group
Dr. Duane Varan, Chief Executive Officer, MediaScience

View the OmniShopper agenda for full session details: https://goo.gl/EqFq4h

Use exclusive LinkedIn discount code OMNI17BL for $100 off the current rate: https://goo.gl/EqFq4h

Subscribe to our monthly insights newsletter, The Insighter: http://bit.ly/2m9UIoG

We hope to see you in Minneapolis!

The OmniShopper Team


Tuesday, February 28, 2017

Image Recognition and the Future of Digital Analytics

This post was originally published on Kelton Global’s Blog.

The days of text-centric social feeds are officially long gone. A whopping 1.8 billion images are uploaded to the Internet daily and of those, 350 million are shared on Facebook. Instagram recently surpassed 500 million active users, and Snapchat now has more active users than Twitter. The content that flows into our social feeds is more heavily optimized than ever to deliver more of what people want—less text and more visuals.

Brands have adapted their social content strategies accordingly by delivering more visually immersive experiences. And while we’re seeing significant shifts in branded content, this influx of visual content has yet to herald a commensurate change in social analytics. Accordingly, few gains have been made to measure and derive insights from the contents of images or video. Social listening has historically focused on the challenges of text-based analysis–specifically, the challenge of determining the context and meaning behind posts. But as social media habits evolve, it’s clear that deriving insights from pictures is an increasingly important aspect of understanding consumers. That’s where image recognition comes into play.

Brands have adapted their social content strategies accordingly by delivering more visually immersive experiences.

Simply put, image recognition is the process of translating images to data. Photos and images can reveal a wealth of data points–demographics, purchases, personalities, and behaviors (just to name a few). Through next generation image recognition, a mere selfie may reveal a person’s gender, approximate age, location disposition, and even the clothing brands that the person is wearing. As text-centric media takes a backseat to image and video, the opportunity to understand the contents of these formats grows. These insights represent a veritable treasure trove of actionable data for brands.

Tools that analyze image and video-based content are still in development, but increased investment in research is already impacting commercial products and how they’re advertised. One example is brand logo recognition–scanning images for brand logos, and flagging them with the corresponding brand names. This tool is especially powerful considering that 80% of photos shared online depict a brand logo but don’t explicitly call out the brand’s name. This fact points to a sizable opportunity for companies to measure and understand the impact of these formerly inaccessible data points.

Photos and images can reveal a wealth of data points–demographics, purchases, personalities, and behaviors (just to name a few).

As an example of how this applies to brands, Kelton’s Digital Analytics team took a look at the scores of backyard BBQ photos that flooded public forums, blogs, and social feeds over the recent 4th of July holiday. We experimented to see which of two quintessentially American beverage brands–Coca-Cola and Budweiser–netted more published images of patriotically-themed bottles and cans (as well as other forms of branding) on social media.

In the end, Coca-Cola branding was twice as prominent as Budweiser’s. We found that Coke bottles and cans popped up in more diverse settings such as public parks and inside motor vehicles, whereas Budweiser was predominantly found in bars and house parties. Coke also aroused greater sentiment around the theme of Americana, as many consumers photographed vintage Coca-Cola gear and opted for bottles over cans. This might explain why Coke captured a significantly greater share of social mentions than Budweiser.

This example illustrates several ways that brands can leverage image recognition technology to build actionable insights:

·         Ethnographic data – Identify where, when and how often brands are showing up in people’s lives.
·         Updated brand health analysis – We now have a more comprehensive point of view of brands’ online footprint.
·         Sponsorship and Branding ROI – Extend the value of branding and sponsorships shared via online news, blogs and social media through a multiplier effect.
·         Influencer identification – Find authentic brand advocates who consume and spotlight your merchandise.
·         Misuse use of brand iconography – Surface content that depicts improper usage of brand’s logo or other creative assets.

In today’s ever-shifting social media landscape, it’s never been more important for brands and their partners to stay aware of the new and emerging capabilities that can help better understand consumers’ behavior online. Image recognition is just the beginning. From AI startups to instant objection recognition devices, the mobilization and fusion of research, tech, and capital is quickly reshaping the way we think about analytics. These new tools will add even more contextual understanding to sentiment on social platforms, empowering brands to understand consumers like never before.

Wednesday, February 22, 2017

The Media Insights & Engagement Conference 2017 Recap

By: Jim Bono, Vice President, Research, Crown Media Family Networks 

Nearly 300 research and insights executives from over 140 different organizations in the media industry gathered in Fort Lauderdale seeking to overcome measurement challenges, uncover the next generation of research methodologies, and create new engagement strategies.

Day 1 recap
MI&E Conference Coordinator, Rachel McDonald, started off the day welcoming this year's attendees and introducing this year's co-chairs: Janet Gallent (NBCUniversal), Rob McLoughlin (POPSUGAR) and Bruce Friend (Maru/Matchbox).

Bruce sat with Turner's Howard Shimmel for a one-on-one discussion about the future of the industry.  Recently, at a Cynopsis conference, Shimmel said "we're at a measurement crisis."  Elaborating on that comment, he explained how it's 2017 and we still do not have a robust cross-platform solution for our industry. Advertisers want an infrastructure that allows more exposure than just reach and frequency.  With Total Audience, we still don't know what to do with it.

They also discussed the Turner Ad Lab, and how people go to Netflix, Hulu, etc., to watch content without ads. What can we do to make the advertising experience better for the consumer?

Howard believes that the industry should have a published document that mandates what currency data research vendors should provide for the content providers. As new platforms are emerging, we need to better understand where those consumers are going to find content.

Bruce asked about big data and how it's all the rage. As an industry where do we go from here?  Howard explained how there is an abundance of research tools out there.  We just haven't done a good enough job telling our clients that we have all these tools.  Big data is a component to an overall data framework. We need to know when to use it and not to use it. Sometimes Big Data can be wrong data.

Bruce also questioned how new companies are great with tech but don't understand the data they deliver. However, other great long-time research companies are very good at analyzing data but don't have the tech.  Howard feels that there's nothing wrong with using a combination of data sets like Nielsen, MRI, and panel data to come up with the best solution. Unfortunately, there are too many companies that reach out and don't really understand our businesses.

He still believes that survey research is important to our industry as data tells what, but not why.

Cathy Cohen, Professor at University of Chicago, gave us a very entertaining look at millennials and the importance of race and ethnicity among this group, especially regarding this year's election. The majority of Millennials in the US are Hispanic and African-American, and by 2060 White will be a minority.
In this past year's election, more African-American and Latino Millennials voted for Democrats, while there were more white Millennials voting Republican. However, in the 2016 primary vote the choice among all Millennials (regardless of ethnicity) was Bernie Sanders.

Cohen's presentation covered
·         The complexity of Millennials through a racial framework
·         Researching race and Millennials
·         Rise of Millennials in the workforce
·         Importance of Millennials in the Political force

Millennials are becoming an increasingly important electoral demographic.  The share of eligible voters that are Millennials has grown during last 3 elections:
·         2008 - 23%
·         2012 - 29%
·         2016 - 36%

Cohen also addressed the six key problems with studying Millennials:
1.        Generational frames / over-representation of white Millennials
2.        Under investigation of white Millennials
3.        Homogenous communities of color missing Millennials
4.        Segmentation of Millennials of color - pick one!
5.        Millennials as experts of Millennials - homophily
6.        One-offs or waves - assumes stability in taste, preferences and decisions

o   Brian Robinson (Facebook)
o   Tom Ciszik (Twitter)
o   Guy Ram (NBC)
o   Leslie Koch (HBO)

Insights from this panel discussion focused on the evolution of social media and how quickly it's grown.
Consumers spend 5.5 hours per week using Social Media on their smartphone.
64% of consumers use smartphone while watching TV. 
1.2 billion interact on Social referring to TV.

After breaking for lunch hour afternoon consisted of Concurrent Tracks.  These case studies were broken into three groups:
·         Track 1 - Targeting Viewers
·         Track 2 - Audience Insights
·         Track 3 - Innovation in Media

The Audience Insights breakouts were:

Ø  HOW STARZ STRECHES RESEARCH FURTHER – Kendra Sindleman, Starz Entertainment

Ø  PUT A SEXY SPIN ON YOUR SALES STORY – Karen Ramspacher, David Tice and Jola Burnett, GfK MRI



The Innovations in Media breakouts were:
Ø  MAXIMIZING AD ENGAGEMENT IN TOTDAY’S CROSS-PLATFORM WORLD – Jon Giegengack and Peter Fondulas, Hub Entertainment Research, and Richard Zackon, CRE




Below are the Track 1 - Targeting Viewers case studies:

David Kaplan from Bravo, along with Zach Schessel from NBCU and Peter Bouchard from Civis Analytics, discussing how to hit the right target audience and "swing" viewers. The presentation also looked at how to attract casual viewers without alienating the core viewers.

Key takeaways were:
·         The different creative approach is often required for on-air vs. off-channel to drive maximum impact with loyal and casual viewers
·         Casual Bravo viewers may all have some affinity for the network but only the "swing viewers" in this group can be readily persuaded to deepen their commitment and watch more
·          An ad’s positive persuadability should be balanced with any potential backlash effects to ensure a net positive effect
·          Not all swing viewers are created equal, e.g. consumers in different DMAs can have a varied response to creative hooks

Steve Schmitt of TiVo showed us how TiVo is helping clients get from traditional linear to non-linear content, and how they improved campaign performance using optimizers and brand targeting. His presentation focused on how:
·          TV consumption has undergone profound changes, especially Millennials age 18-34
·          Total video consumption continues to expand with DVR, VOD, SVOD and online/mobile viewing extending the power of linear TV
·          Linear TV has majority share, but it is declining as on-demand options expand

Concepts on the rise are binge viewing, on-demand, cord-cutting and cord-shaving, while things like appointment viewing and one-size-fits-all on decline.

Darlene LaChapelle and Maya Abinakad from AOL talked about the top drivers for video growth, with "social media video offerings" and "better quality creative" leading the way, and how online video growth is driven by mobile devices.
·         Online video viewing on a smartphone is on par with that of a computer
·          Consumers indicate they have few technical barriers watching online video on their smartphones, but get the convenience of watching anywhere, anytime
·          62% said I watch more online video today than one year ago
·          62% said in the next 6 months I expect to watch more online video

Laptop/desktop (70%) is still the leading device on which online video is watch daily, just edging smartphone (67%)

Our afternoon continued with our only Track 1 panel.  The panel was moderated by Horowitz's Adriana Waterson, and we heard from Michele Meyer (Univision), Tom Kralik (Revolt) and Lia Silkworth (Telemundo) as they discussed their key takeaways about multicultural millennials and the importance of this audience in our business today, as leading consumers of cross-platform media.
·          Hispanics are leading the charge in cross-platform media consumption
·          Millennial and Gen Z trends ARE multicultural trends
·         Gen Z is more diverse and multicultural and are digital natives
·          If you join a multicultural network, your general market skills may not "translate"

Our first day concluded with this presentation from Chris Kelly at Survata.

Day 2 recap
Co-chair Rob McLoughlin opened the morning with a recap for Day 1, and a look at what to expect for Day 2.

Amber Case, author of Design for the Next Generation of Devices, gave us a comical look at connected devices and how the average consumer has become dependent on them.  She showed us products like PetNet, and how the Web and technology play a major role in self-development.

In this world of ever changing technology, we need to make sure that “machines shouldn’t act like humans, and humans shouldn’t act like machines.”

Edwin Wong of Buzzfeed gave us his insights on Recoding Culture.  We got a look at Millennials and how culture is being reshaped and where it's headed.
76% of Gen Y say "it's the norm to be radical" (as opposed to 60% of Gen X).

Buzzfeed conducted a study breaking millennials into 4 groups:
o   Omegas
o   Sigma’s
o   Cult Kids
o   Nichesters
And we found that there are strong overlaps between these groups.

Wong stressed how we're moving towards the end of demographics, evolution of psychographics and the rise of the individual.

He ended his keynote with a very touching video about Asians and their stories about the sacrifices their parents made for them.

Tobin Trevarthen of 21st Century Narrative and author of Narrative Generation was our next keynote speaker and covered:
·         what is a narrative
·         why you need a narrative
·         story vs. Narrative
·         building a narrative

A narrative differs from a story.  More directly, a narrative is a mosaic of related, contextual stories that inform and define one's perspective.
A story has a beginning, a middle and an end.  A story has a plot, and acts as a one-way monologue.
A narrative is endless, and has a more interactive dialogue.

Tobin showed how Tesla automotive expanded the brand narrative to reach consumers.

Mainak Mazumdar, CRO of Nielsen, was our last keynote speaker of the morning.  Mazumdar explained how recently data sets had errors and inaccuracies in station crediting, time shifted content and missing live viewing.  He addressed 2 key questions:
·         what is our "ground truth?
·         how do we understand and correct for biases?

Nielsen used RPD data along with 200,000+ high quality person's panel to address methodology challenges.
His RPD data and panel findings showed that:
·         20% of live RPD minutes were credited to the wrong station
·         25% of live viewing in the RPD was missing
·         40% of time shifted viewing was credited to the wrong content

Nielsen is working hard to understand and correct these inaccuracies.

The Day 2 afternoon Audience Insights breakouts were:

Ø  WHY CO-VIEWING MATTERS – Marc Normand, Disney-Freeform and Brian West, Disney ABC



The Innovations in Media breakouts were:
Ø  INSIGHTS OR INSANITY IN THE AGE OF COMPLEXITY – James Petretti, Sony Pictures Television


Ø  MARKETING TV NEWS RELEVANT TO NEW GENERATIONS – Kimberly Maxwell, NBC News, Sam Ford, MIT Comparative and Peggy Einnehmer, LRW

Ø  FUTURE OF ONLINE VIDEO – David Dowd, Tubular Labs

Below are the Track 1 - Targeting Viewers case studies:

Jason Shalaveyus from Starcom and Nicole Tramontano from Turner showed us how agencies and media companies need to understand how consumer video ad experiences keep pace with content experiences. 
Despite the industry pendulum swing away from engaged reach towards efficiency and programmatic buying in recent years, Starcom and Turner set out to determine:
·         Relative importance of contextual factors that influence ad receptivity
·         Range of impact for individual factors
·         Net effect of multiple factors
·         Prevalence of optimal contexts among segments
·         Whether contextual relevance can improve upon category relevance
·         If ceding even more control to the viewer improved the overall viewing experience

Top findings:
·         Easy wins where you have high control over highly influential factors are hard to come by
·         Life environments affect receptivity more than ad environments
·         Content has a stable shelf life, but ads spoil quickly
·         Relevance is important both in the market and in the moment
·         The cat is out of the bag as far as control, but leashes can work

In summary:
A rising tide lifts all boats.
Don’t neglect the impact of context.
Be selective.
Be Flexible.

Armida Ascano and Gil Haddi from Trend Hunter are helping clients find the stories that connect them to Gen Z (infants to 17) - what defines them and what they mean to Media.  They are not as big as Millennials, but they are just as important.  By 2020, Gen Z will be 40% of the consumer base.

They explained the overall differences between to two age group.
Online Presence:
·         Gen Y – Facebook (overshare)
·         Gen Z – Snapchat (private)
Media Consumption:
·         Gen Y – Love content
·         Gen Z – Really, really love content
Outlook on Life:
·         Gen Y – Laissez faire
·         Gen Z – Cautiously planning

Gen Z is the most diverse generation, and they are underrepresented in the mainstream media. As a result, they turn to influencers who look and speak like them.

They already have the tools, creativity and desire to create, but do not enjoy passive media consumption.

This generation is swapping in aspiration for realism.  As content providers, we need to choose influencers and messaging with this in mind.

A nearly packed room showed up to see Melanie Schneider (AMC) and Stephanie Yates (WE) present their case study.
“TV is Dead! Run for the Hills!” “Cord-cutting Means the End of Linear!” “Cable TV as We Know it is Dying!”

These are the comments we hear in the press everyday about our industry.  And it’s true that TV viewership has shown downward declines over the past 5 years.  However, content is up more than ever.  How are we able to watch all this content?  Technology has propelled viewer choice.

AMC Networks did a study focusing on content, taking a deeper dive into Nielsen respondent level data exploring viewers, their habits, and how they watch content.

Tamara Barber from Simmons Research gave us a presentation explaining that video consumption is not just linear and live anymore. 
The majority of the share of Broadcast viewing still comes from Live (35%) and DVR playback up to 7 days (34%).  The same holds true for Cable, with 43% viewing done Live and 26% coming from DVR playback in the first 7 days.  However, there is still a large market opportunity for DVR after 7 days, VOD after 3 days, and OTT.

Simmons looked at comprehensive video measurement across linear, SVOD, OTT and other connected devices.

OTT users are psychographically different. The Top 10 OTT user attributes included:
·         more digital
·         more social media
While the Top 10 attributes for non-OTT users included:
·         use cell phone for calling only
·         read newspaper daily 

Simmons is hoping to use psychographics to optimize Media planning and buying.

Day 3 recap
Day 3 started with co-chair Bruce Friend recapping Day 2, then introducing today's first keynote speaker.

Paul Depodesta, CSO of Cleveland Browns, engaged the audience with an overview that there's a certain way that things work.  Whether baseball, black jack, or other situations in life, there’s always that “rule of thumb” that we are taught to follow.  However, sometimes the 'rule' doesn't always work.  It's all about the process. Paul described a process/outcome quad:

·         Good process/ Good outcome = success
·         Good process/ Bad outcome = just unlucky
·         Bad process/ Good outcome = get lucky once, but then rely on that luck to be successful again
·         Bad process/ Bad outcome = recipe for failure 

So, how do you win with a lack of resources? 
Putting together a championship team is like cooking a gourmet meal - you need the right ingredients. 
We're always asking the naive questions- why is the market down, why is this player struggling? We need a reason, but there not always is a reason, so we try to explain by creating our own cause and relationships.

As with The Oakland A’s in Moneyball, sometimes we need to throw out the old metrics, that “rule of thumb” and start new.  Key takeaways he learned from testing these new metrics were:         
·         Find skillful affordable talent to replace high priced starts
·         Statistics can be misleading

He drew comparisons of scouting baseball players to testing programs.  Emotions drive our decisions, and we tend to look for data to support and confirm these decisions, while dismissing any data that contradicts what we believe.

Paul left us with these 3 points: 
·         become aware of biases
·         become relentless in asking the naive question
·         in the game of uncertainty, how can we beat the house? Learn by previous failures to better hit success.

The late morning keynote was actually broken into 3 parts.  Robin Garfield of CNN spoke first, and then we heard Dr. John Lapinski from NBC News, followed by a Q&A with our 2 speakers.

Millennials told us they wanted a candidate who has a plan to:
ü  Create good paying jobs
ü  Make healthcare more affordable
ü  Do something about the soaring costs of higher education and student debt
Millennials also told us they didn’t want a candidate who:
ü  Represents “more of the same”
They were looking for a transformational candidate - someone who will “change the government”, and that they were “done with the Clintons and Bushes.”
Most Millennials liked Bernie Sanders, and both Trump and Clinton were viewed negatively.

Not only was 2016 the most watched year on record in cable news (with over 3 million total P2+ aggregate audience), but more people came out to vote in 2016 than ever before.
·         2000 – 105.4 million total turnout (54.2% of eligible population that voted)
·         2004 – 122.3 million (60.1%)
·         2008 – 131.3 million (61.6%)
·         2012 – 129,1 million (58.6%)
·         2016 – 136.6 million (59.0%)

We were show examples of “what-if” scenarios, that demonstrated how close the election really was.
While Clinton’s popular vote lead was just shy of 3 million (65.8 million for Clinton compared to 63.0 million for Trump), the red/blue map showed that the majority of Clinton’s popular vote came from New York and California.  And the 2016 Electoral College hinged on a handful of states, with Trump taking Florida and the Rust Belt states (Iowa, Michigan, Ohio, Pennsylvania and Wisconsin).

Jane Clark, from the Coalition for Innovative Media Measurement, moderated this panel which included:
Jed Meyer (Univision), Jonathan Steuer (Omnicom), Carol Hinnant (comScore),
Steven Schmitt (TiVo) and Kelly Abcarian (Nielsen).

The panel gave us a perspective of the industry from the network, agency, and measurement side.  They addressed the integrity of data and optimizing tools for better plans.  They talked about how there’s a constant struggle trying to bring all measurement across all platforms together.

Kelly stressed how measurement needs to be a team sport.  Media companies are more and more starting to own their own data, and that changes the dynamic of the industry.

There is a call from the network and agency side for duration weighted viewable impressions across all platforms, and the measurement companies just aren’t there yet.  The question remains – how do we get there?

The Day 3 afternoon Audience Insights breakouts were:


The Innovations in Media breakouts were:

Below are the Track 1 - Targeting Viewers case studies:

ESPN's David Hobbie gave us insight to David's study focused on an advertising campaign during this past year's Olympics in Rio, and the impact and brand lift experienced on ESPN Latin America.

The last case study track of the conference had Theresa Pepe of Viacom give us an in depth look at kids’ data and... The Story of Me.

We learned about kids under 11 and how they are the most diverse kids ever. They make up 15.4% of the US population, and are extremely persuasive. 
Theresa showed us a breakdown of these kids focusing on:
·         My beginning
·         My world
·         My family
·         Myself
·         My friends
·         My tech
·         My dreams
·         Me in a nutshell. 

Since they were born these kids experienced: 
- The first Black president 
- Terrorism
- Marriage equality 
- Great recession 
- YouTubers 
- On demand 
- Social Media 
- Device overload 
- Gender neutrality 

Their role models are their families… and some celebrities.  While 78% of girls look up to mom, on 58% of boys look up to dad.  26% said the look up to a grandparent, while the rest of their role models included YouTube/Vine stars (19%), teacher (18%), brother (17%), sister (15%), aunt/uncle/cousin (13%), actor/actress (10%), athlete (10%).

And they are busy!  6.2 hours of the day they are in school, while the rest of their day entails sleeping (8.7 hours), eating/traveling (1.7 hours), organized sports/activities (.9 hours), doing homework (.8 hours), and 6.4 hours going towards leisure (26% of their day.)

In their free time, they watch TV (48%), play with toys (43%), play video games (33%), and play outside (18%).


The Conference concluded with a wrap-up with the year’s co-chairs and the advisory panel giving their feedback of the sessions, discussing plans for next year’s conference, and taking questions from the audience.